{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imitation Learning for Portfolio Management using CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Steps to try:**\n",
    "1. We first try to overfit a 3 stocks toy case using 3 years of training data.\n",
    "2. Try to generalize to other years of the same stock.\n",
    "3. Get some insigts on network topology and hyperparameter tuning.\n",
    "4. Increase the number of stocks. (Increase action space to more than 10.)\n",
    "\n",
    "**Possible improvement methods:**\n",
    "1. Use correlated action noise\n",
    "2. Use adaptive parameter noise\n",
    "\n",
    "**Figures to show:**\n",
    "1. Training: total rewards w.r.t episode\n",
    "2. How the model performs on training data\n",
    "3. How the model performs on testing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from utils.data import read_stock_history, index_to_date, date_to_index\n",
    "import matplotlib.pyplot as plt\n",
    "# for compatible with python 3\n",
    "from __future__ import print_function\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# force tensorflow to use CPU\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"   # see issue #152\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data and choose the target stocks for training a toy example\n",
    "history, abbreviation = read_stock_history(filepath='utils/datasets/stocks_history_target.h5')\n",
    "history = history[:, :, :4]\n",
    "target_stocks = ['AAPL', 'CMCSA', 'REGN']\n",
    "training_date_start = '2012-08-13'\n",
    "training_date_end = '2015-08-13'  # three years training data\n",
    "training_index_start = date_to_index(training_date_start)\n",
    "training_index_end = date_to_index(training_date_end)\n",
    "target_history = np.empty(shape=(len(target_stocks), training_index_end - training_index_start, history.shape[2]))\n",
    "for i, stock in enumerate(target_stocks):\n",
    "    target_history[i] = history[abbreviation.index(stock), training_index_start:training_index_end, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# collect testing data\n",
    "testing_date_start = '2015-08-13'\n",
    "testing_date_end = '2017-08-12'\n",
    "testing_index_start = date_to_index(testing_date_start)\n",
    "testing_index_end = date_to_index(testing_date_end)\n",
    "testing_history = np.empty(shape=(len(target_stocks), testing_index_end - testing_index_start, history.shape[2]))\n",
    "for i, stock in enumerate(target_stocks):\n",
    "    testing_history[i] = history[abbreviation.index(stock), testing_index_start:testing_index_end, :]\n",
    "# normalize\n",
    "for i in range(target_history.shape[0]):\n",
    "    for j in range(target_history.shape[1]):\n",
    "        target_history[i][j] = target_history[i][j]/np.linalg.norm(target_history[i][j])\n",
    "for i in range(testing_history.shape[0]):\n",
    "    for j in range(testing_history.shape[1]):\n",
    "        testing_history[i][j] = testing_history[i][j]/np.linalg.norm(testing_history[i][j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 1095, 4)\n",
      "(3, 730, 4)\n"
     ]
    }
   ],
   "source": [
    "print(target_history.shape)\n",
    "print(testing_history.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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kAG0ANgCYos4notkADgDQm4gaAFzOzDMB/A7AcgCv+Lb6R5h5KoALAXyNiNoA7ABwMZfT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\noahQoUKFCnXx/5os6yp56KmkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8aa0114a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TOZ9IiqAUwrrthRxxMaJ/sN6Zyur+8qXE/tvslXmdP2PRJry23NLU1NF5pLkh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a7406ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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d8scAIdEO5g+y6VvEew/XSKfu1ZEZ2sKu3J+Y6DtNAAsfyI0c5A4rT1V0QY4l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a71fd5e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize 3 stock open price\n",
    "date_list = [index_to_date(i) for i in range(target_history.shape[1])]\n",
    "x = range(target_history.shape[1])\n",
    "for i in range(len(target_stocks)):\n",
    "    plt.figure(i)\n",
    "    plt.plot(x, target_history[i, :, 1])  # open, high, low, close = [0, 1, 2, 3]\n",
    "    plt.xticks(x[::200], date_list[::200], rotation=30)\n",
    "    plt.title(target_stocks[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "0.0025\n"
     ]
    }
   ],
   "source": [
    "from environment.portfolio import PortfolioEnv\n",
    "env = PortfolioEnv(target_history, target_stocks, window_length = 1)\n",
    "print(env.window_length)\n",
    "print(env.num_stocks)\n",
    "print(env.sim.cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape training and testing data\n",
    "trainX = np.zeros(((target_history.shape[1]-env.window_length)*(env.num_stocks+1), 4, env.window_length+1, env.num_stocks))\n",
    "testX = np.zeros(((testing_history.shape[1]-env.window_length)*(env.num_stocks+1), 4, env.window_length+1, env.num_stocks))\n",
    "trainY = np.zeros(((target_history.shape[1]-env.window_length)*(env.num_stocks+1), env.num_stocks+1))\n",
    "testY = np.zeros(((testing_history.shape[1]-env.window_length)*(env.num_stocks+1), env.num_stocks+1))\n",
    "for i in range(target_history.shape[1]-env.window_length):\n",
    "    for stockHeld in range(env.num_stocks + 1):\n",
    "        for j in range(4):\n",
    "            for windowIndex in range(env.window_length):\n",
    "                for stock in range(env.num_stocks):\n",
    "                    trainX[i*(env.num_stocks+1)+stockHeld][j][windowIndex][stock] = target_history[stock][i+env.window_length-windowIndex-1][j]\n",
    "            if stockHeld > 0:\n",
    "                trainX[i*(env.num_stocks+1)+stockHeld][j][env.window_length][stockHeld-1] = 1\n",
    "\n",
    "for i in range(testing_history.shape[1]-env.window_length):\n",
    "    for stockHeld in range(1, env.num_stocks + 1):\n",
    "        for j in range(4):\n",
    "            for windowIndex in range(env.window_length):\n",
    "                for stock in range(env.num_stocks):\n",
    "                    testX[i*(env.num_stocks+1)+stockHeld][j][windowIndex][stock] = testing_history[stock][i+env.window_length-windowIndex-1][j]\n",
    "            if stockHeld > 0:\n",
    "                testX[i*(env.num_stocks+1)+stockHeld][j][env.window_length][stockHeld-1] = 1\n",
    "for i in range(target_history.shape[1]-env.window_length):\n",
    "    for stockHeld in range(env.num_stocks+1):\n",
    "        bestRate = 1.0\n",
    "        bestStock = -1\n",
    "        for stock in range(env.num_stocks):\n",
    "            trading_cost = env.sim.cost\n",
    "            if stockHeld-1 == stock:\n",
    "                trading_cost = 0\n",
    "            # print(trainX[i*(env.num_stocks+1) + stockHeld][3][0][stock], target_history[stock][i+env.window_length-1][3])\n",
    "            rate = target_history[stock][i+env.window_length-1][3]/target_history[stock][i+env.window_length-1][0] - trading_cost\n",
    "            if rate > bestRate:\n",
    "                bestRate = rate\n",
    "                bestStock = stock\n",
    "        trainY[i*(env.num_stocks+1) + stockHeld][bestStock+1] = 1\n",
    "for i in range(testing_history.shape[1]-env.window_length):\n",
    "    for stockHeld in range(env.num_stocks+1):\n",
    "        bestRate = 1.0\n",
    "        bestStock = -1\n",
    "        for stock in range(env.num_stocks):\n",
    "            trading_cost = env.sim.cost\n",
    "            if stockHeld-1 == stock:\n",
    "                trading_cost = 0\n",
    "            rate = testing_history[stock][i+env.window_length-1][3]/testing_history[stock][i+env.window_length-1][0] - trading_cost\n",
    "            if rate > bestRate:\n",
    "                bestRate = rate\n",
    "                bestStock = stock\n",
    "        testY[i*(env.num_stocks+1) + stockHeld][bestStock+1] = 1\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "Using Theano backend.\n",
      "WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10).  Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n",
      " https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n",
      "\n",
      "Using gpu device 0: Tesla K80 (CNMeM is disabled, cuDNN 5110)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Built model from scratch\n"
     ]
    }
   ],
   "source": [
    "from model.supervised.cnn import CNN\n",
    "# instantiate CNN model\n",
    "cnn_model = CNN(env=env)\n",
    "cnn_model.build_model(load_weights=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "4376/4376 [==============================] - 4s - loss: 5.8845 - acc: 0.2347     \n",
      "Epoch 2/10\n",
      "4376/4376 [==============================] - 4s - loss: 6.5143 - acc: 0.2514     \n",
      "Epoch 3/10\n",
      "4376/4376 [==============================] - 4s - loss: 5.4075 - acc: 0.2278     \n",
      "Epoch 4/10\n",
      "4376/4376 [==============================] - 4s - loss: 6.1577 - acc: 0.2356     - ETA: \n",
      "Epoch 5/10\n",
      "4376/4376 [==============================] - 4s - loss: 5.3662 - acc: 0.2242     \n",
      "Epoch 6/10\n",
      "4376/4376 [==============================] - 4s - loss: 4.1719 - acc: 0.2246     \n",
      "Epoch 7/10\n",
      "4376/4376 [==============================] - 4s - loss: 3.9642 - acc: 0.2253     \n",
      "Epoch 8/10\n",
      "4376/4376 [==============================] - 4s - loss: 3.9535 - acc: 0.2233     \n",
      "Epoch 9/10\n",
      "4376/4376 [==============================] - 4s - loss: 4.0419 - acc: 0.2230     \n",
      "Epoch 10/10\n",
      "4376/4376 [==============================] - 4s - loss: 3.9527 - acc: 0.2228     \n",
      "Finish.\n"
     ]
    }
   ],
   "source": [
    "# starts to train the model, hopefully it would work\n",
    "cnn_model.train(trainX, trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[ 0.  0.  0.  1.] [ 0.  1.  0.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  1.  0.  0.]\n",
      "[ 0.22769251  0.21792054  0.15906529  0.3953217 ] [ 0.  1.  0.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  1.  0.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  0.  1.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  1.  0.  0.]\n",
      "[ 0.22769251  0.21792054  0.15906529  0.3953217 ] [ 0.  0.  1.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  0.  1.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  0.  1.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  0.  1.  0.]\n",
      "[ 0.22769251  0.21792054  0.15906529  0.3953217 ] [ 0.  0.  1.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  0.  1.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  0.  1.  0.]\n",
      "[ 0.  0.  0.  1.] [ 0.  0.  1.  0.]\n",
      "[8.5830439317700638, 0.31652949245541839]\n"
     ]
    }
   ],
   "source": [
    "# test model\n",
    "predictions = cnn_model.predict(testX)\n",
    "for i in range(len(predictions)):\n",
    "    print(predictions[i], testY[i])\n",
    "score = cnn_model.evaluate(testX, testY)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ddpg_model_from_file = DDPG(env=env)\n",
    "ddpg_model_from_file.build_model(load_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# evaluate the model with training data\n",
    "env = PortfolioEnv(target_history, target_stocks)\n",
    "observation, action = env.reset()\n",
    "done = False\n",
    "while not done:\n",
    "    observation = observation[:, :, 3] / observation[:, :, 0]\n",
    "#     print(observation)\n",
    "    observation = np.expand_dims(observation, axis=-1)\n",
    "    action = ddpg_model_from_file.predict(observation)\n",
    "    action = np.squeeze(action, axis=0)\n",
    "#     print(action)\n",
    "#     input('Press any key to continue...')\n",
    "    observation, _, done, _ = env.step(action)\n",
    "env._render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# evaluate the model with unseen data from same stock\n",
    "env = PortfolioEnv(testing_history, target_stocks, steps=650, start_idx=testing_index_start, \n",
    "                   sample_start_date='2015-10-02')\n",
    "observation, action = env.reset()\n",
    "done = False\n",
    "for i in range(10):\n",
    "    observation = observation[:, :, 3] / observation[:, :, 0] * 100\n",
    "    observation = np.expand_dims(observation, axis=-1)\n",
    "    action = ddpg_model_from_file.predict(observation)\n",
    "    action = np.squeeze(action, axis=0)\n",
    "#     print(action)\n",
    "    observation, _, done, _ = env.step(action)\n",
    "env._render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savez('temp/total_reward_state_1.npz', stat=ddpg_model.total_reward_stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plot episode reward and save it\n",
    "total_reward_stat = np.load('temp/total_reward_state_1.npz')['stat']\n",
    "x = range(len(total_reward_stat))[::20]\n",
    "plt.plot(x, total_reward_stat[::20])\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Reward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# collect testing data of another 3 different companies\n",
    "target_stocks = ['GOOGL', 'CSX', 'MAR']\n",
    "testing_date_start = '2012-08-13'\n",
    "testing_date_end = '2017-08-12'\n",
    "testing_index_start = date_to_index(testing_date_start)\n",
    "testing_index_end = date_to_index(testing_date_end)\n",
    "testing_history = np.empty(shape=(len(target_stocks), testing_index_end - testing_index_start, history.shape[2]))\n",
    "for i, stock in enumerate(target_stocks):\n",
    "    testing_history[i] = history[abbreviation.index(stock), testing_index_start:testing_index_end, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# evaluate the model with unseen data from different stocks\n",
    "env = PortfolioEnv(testing_history, target_stocks, steps=1700, start_idx=testing_index_start)\n",
    "observation, action = env.reset()\n",
    "done = False\n",
    "while not done:\n",
    "    observation = observation[:, :, 3] / observation[:, :, 0]\n",
    "    observation = np.expand_dims(observation, axis=-1)\n",
    "    action = ddpg_model.predict(observation)\n",
    "    action = np.squeeze(action, axis=0)\n",
    "    observation, _, done, _ = env.step(action)\n",
    "env._render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ddpg_model.actor.model.save_weights(ddpg_model.actor_path)\n",
    "ddpg_model.critic.model.save_weights(ddpg_model.critic_path)\n",
    "ddpg_model.actor.target_model.save_weights(ddpg_model.actor_target_path)\n",
    "ddpg_model.critic.target_model.save_weights(ddpg_model.critic_target_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "layer_index = 1\n",
    "# check model load\n",
    "ddpg_model.actor.model.get_layer(index=layer_index).get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ddpg_model_from_file.actor.model.get_layer(index=layer_index).get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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